Abstract: Computing statistical dependence of terms in textual
documents is a widely studied subject and a core problem
in many areas of science. This study focuses on such a
problem and explores the techniques of estimation using the
expected mutual information measure. A general framework is
established for tackling a variety of estimations: (i) general forms
of estimation functions are introduced; (ii) a set of constraints
for the estimation functions is discussed; (iii) general forms of
probability distributions are defined; (iv) general forms of the
measures for calculating mutual information of terms (MIT)
are formalised; (v) properties of the MIT measures are studied
and, (vi) relations between the MIT measures are revealed. Four
estimation methods, as examples, are proposed and mathematical
meanings of the individual methods are respectively interpreted.
The methods may be directly applied to practical problems for
computing dependence values of individual term pairs. Due to its
generality, our method is applicable to various areas, involving
statistical semantic analysis of textual data.